from_torch
from_torch#
This module provides support for importing models into the sinabs from pytorch.
- class sinabs.from_torch.SpkConverter(input_shape=None, spike_threshold=1.0, min_v_mem=- 1.0, membrane_subtract=None, bias_rescaling=1.0, num_timesteps=None, batch_size=1, synops=False, add_spiking_output=False, backend='bptt', kwargs_backend=None)#
Converts a Torch model and returns a Sinabs network object. The modules in the model are analyzed, and a copy is returned, with all ReLUs, LeakyReLUs and NeuromorphicReLUs turned into SpikingLayers.
- Parameters
input_shape – If provided, the layer dimensions are computed. Otherwise they will computed at the first forward pass.
spike_threshold – The membrane potential threshold for spiking in convolutional and linear layers (same for all layers).
min_v_mem – The lower bound of the potential in convolutional and linear layers (same for all layers).
membrane_subtract – Value subtracted from the potential upon spiking for convolutional and linear layers (same for all layers).
bias_rescaling – Biases are divided by this value.
num_timesteps – Number of timesteps per sample. If None, batch_size must be provided to seperate batch and time dimensions.
batch_size – Must be provided if num_timesteps is None and is ignored otherwise.
synops – If True (default: False), register hooks for counting synaptic operations during foward passes.
add_spiking_output – If True (default: False), add a spiking layer to the end of a sequential model if not present.
backend – String defining the simulation backend (currently sinabs or exodus)
kwargs_backend – Dict with additional kwargs for the simulation backend
- convert(model)#
Converts the Torch model and returns a Sinabs network object.
- Returns network
the Sinabs network object created by conversion.
- sinabs.from_torch.from_model(model, input_shape=None, spike_threshold=1.0, min_v_mem=- 1.0, membrane_subtract=None, bias_rescaling=1.0, num_timesteps=None, batch_size=1, synops=False, add_spiking_output=False, backend='sinabs', kwargs_backend=None)#
Converts a Torch model and returns a Sinabs network object. The modules in the model are analyzed, and a copy is returned, with all ReLUs, LeakyReLUs and NeuromorphicReLUs turned into SpikingLayers.
- Parameters
model – a Torch model
input_shape – If provided, the layer dimensions are computed. Otherwise they will be computed at the first forward pass.
spike_threshold – The membrane potential threshold for spiking in convolutional and linear layers (same for all layers).
min_v_mem – The lower bound of the potential in convolutional and linear layers (same for all layers).
membrane_subtract – Value subtracted from the potential upon spiking for convolutional and linear layers (same for all layers).
bias_rescaling – Biases are divided by this value.
num_timesteps – Number of timesteps per sample. If None, batch_size must be provided to seperate batch and time dimensions.
batch_size – Must be provided if num_timesteps is None and is ignored otherwise.
synops – If True (default: False), register hooks for counting synaptic operations during forward passes.
add_spiking_output – If True (default: False), add a spiking layer to the end of a sequential model if not present.
backend – String defining the simulation backend (currently sinabs or exodus)
kwargs_backend – Dict with additional kwargs for the simulation backend